Nguyen Bich Ngoc, Jacques Teller 13 December 2021
inccatdf <- df[!(is.na(df$inccat)),] %>%
group_by(inccat) %>%
summarise(count = n(),
prop = n()/nrow(df),
income_avr = mean(income),
income_min = min(income),
income_max = max(income),
inceqa_avr = mean(inceqa),
inceqa_min = min(inceqa),
inceqa_max = max(inceqa))
inccatdf## # A tibble: 4 x 9
## inccat count prop income_avr income_min income_max inceqa_avr
## <fct> <int> <dbl> <dbl> <int> <int> <dbl>
## 1 precarious 148 0.0970 1135. 125 2250 8854.
## 2 modest 697 0.457 1881. 1250 3250 15961.
## 3 average 501 0.329 3203. 2250 4750 23622.
## 4 higher 178 0.117 4739. 3750 5250 30957.
## # ... with 2 more variables: inceqa_min <dbl>, inceqa_max <dbl>
| Utilities | Number of households | CVD | CVA | Average price | Block 1 price | Block 2 price | Block2/Block1 |
|---|---|---|---|---|---|---|---|
| SWDE | 1138 | 2.4480 | 1.745 | 4.4551 | 1.2240 | 4.1930 | 3.4257 |
| CILE | 261 | 2.6366 | 1.745 | 4.6523 | 1.3183 | 4.3816 | 3.3237 |
| IECBW | 126 | 2.1600 | 1.745 | 4.0727 | 1.0800 | 3.9050 | 3.6157 |
## Warning: Removed 192 row(s) containing missing values (geom_path).
## Warning: Removed 150 row(s) containing missing values (geom_path).
| Quintile | Number of households | Number of people | Min income (EUR/month) | Max income (EUR/month) |
|---|---|---|---|---|
| 1 | 305 | 474 | 125 | 1250 |
| 2 | 305 | 602 | 1250 | 2250 |
| 3 | 305 | 734 | 2250 | 2750 |
| 4 | 305 | 845 | 2750 | 3750 |
| 5 | 305 | 1006 | 3750 | 5250 |
# Correlation between water consumption and household income should use spearman?????
cor.test(df$csmptv, df$income, method = "pearson")##
## Pearson's product-moment correlation
##
## data: df$csmptv and df$income
## t = 15.505, df = 1523, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3250574 0.4117940
## sample estimates:
## cor
## 0.3692295
cor.test(df$csmptv, df$income, method = "spearman")## Warning in cor.test.default(df$csmptv, df$income, method = "spearman"):
## Cannot compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: df$csmptv and df$income
## S = 358665135, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.3932203
# Correlation between water consumption and income per equivalent adult should use spearman?????
cor.test(df$csmptv, df$inceqa, method = "pearson")##
## Pearson's product-moment correlation
##
## data: df$csmptv and df$inceqa
## t = 1.8473, df = 1523, p-value = 0.06489
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.00291998 0.09724963
## sample estimates:
## cor
## 0.0472837
cor.test(df$csmptv, df$inceqa, method = "spearman")## Warning in cor.test.default(df$csmptv, df$inceqa, method = "spearman"):
## Cannot compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: df$csmptv and df$inceqa
## S = 547594684, p-value = 0.004034
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.07359449
## Warning: Removed 9 rows containing non-finite values (stat_boxplot).
## Warning: Removed 9 rows containing non-finite values (stat_summary).
## Warning: Removed 9 rows containing non-finite values (stat_summary).
## Warning: Removed 9 rows containing non-finite values (stat_boxplot).
## Warning: Removed 9 rows containing non-finite values (stat_summary).
## Warning: Removed 9 rows containing non-finite values (stat_summary).
summary(df$avrprc)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.955 4.324 4.419 4.910 4.576 20.055
summary(df$avrprc[df$poorest == 1])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.076 4.416 4.551 5.606 5.499 16.277
summary(df$avrprc[df$inccat == "precarious"])## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 3.971 4.332 4.457 5.098 4.665 16.277 1
summary(df$subs[df$inccat == "precarious"])## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -79.564 -4.286 1.840 -3.918 10.142 30.484 1
summary(df$mgnprc)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.080 4.193 4.193 3.693 4.193 4.382
summary(df$mgnprc[df$poorest == 1])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.080 1.224 4.193 3.155 4.193 4.382
summary(df$mgnprc[df$inccat == "precarious"])## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.080 3.905 4.193 3.478 4.193 4.382 1
## 3.5. changing fixed -----
### new cvd ------| SWDE | CILE | IECBW | |||
|---|---|---|---|---|---|
| As in 2014 | 101.4797 | 2.4480 | 2.6366 | 2.1600 | 1.745 |
| 1 | 0.0000 | 4.3356 | 4.5642 | 3.7129 | 1.745 |
| 2 | 50.0000 | 3.4040 | 3.6470 | 2.9003 | 1.745 |
| 3 | 100.0000 | 2.4724 | 2.7298 | 2.0877 | 1.745 |
| 4 | 150.0000 | 1.5408 | 1.8126 | 1.2751 | 1.745 |
| 5 | 200.0000 | 0.6092 | 0.8954 | 0.4625 | 1.745 |
| SWDE | CILE | IECBW | |||
|---|---|---|---|---|---|
| 0 | 101.1914 | 2.4480 | 2.6366 | 2.1600 | 1.745 |
| 50 | 95.4887 | 2.1349 | 2.4669 | 1.8680 | 1.745 |
| 100 | 89.7860 | 1.8218 | 2.2972 | 1.5759 | 1.745 |
| 150 | 84.0834 | 1.5087 | 2.1276 | 1.2839 | 1.745 |
| 200 | 78.3807 | 1.1956 | 1.9579 | 0.9919 | 1.745 |
| SWDE | CILE | IECBW | fixed | revincr |
|---|---|---|---|---|
| 4.455142 | 4.652346 | 4.072663 | 0 | 0.0 |
| 3.717046 | 3.923825 | 3.416345 | 50 | 0.0 |
| 2.978950 | 3.195304 | 2.760027 | 100 | 0.0 |
| 5.346171 | 5.582816 | 4.887196 | 0 | 0.2 |
| 4.608075 | 4.854294 | 4.230878 | 50 | 0.2 |
| 3.869979 | 4.125773 | 3.574559 | 100 | 0.2 |
| 6.682713 | 6.978520 | 6.108995 | 0 | 0.5 |
| 5.944617 | 6.249999 | 5.452677 | 50 | 0.5 |
| 5.206522 | 5.521477 | 4.796358 | 100 | 0.5 |
| SWDE | CILE | IECBW | fixed | revincr |
|---|---|---|---|---|
| 1.809970 | 1.882461 | 1.604395 | 0 | 0.0 |
| 1.510107 | 1.587682 | 1.345843 | 50 | 0.0 |
| 1.210244 | 1.292903 | 1.087292 | 100 | 0.0 |
| 2.171964 | 2.258953 | 1.925274 | 0 | 0.2 |
| 1.872101 | 1.964174 | 1.666723 | 50 | 0.2 |
| 1.572239 | 1.669396 | 1.408171 | 100 | 0.2 |
| 2.714955 | 2.823691 | 2.406593 | 0 | 0.5 |
| 2.415092 | 2.528913 | 2.148041 | 50 | 0.5 |
| 2.115229 | 2.234134 | 1.889489 | 100 | 0.5 |
| SWDE | CILE | IECBW | fixed | revincr |
|---|---|---|---|---|
| 1.839406 | 1.875260 | 1.611007 | 0 | 0.0 |
| 1.534667 | 1.581609 | 1.351390 | 50 | 0.0 |
| 1.229927 | 1.287958 | 1.091772 | 100 | 0.0 |
| 2.207288 | 2.250312 | 1.933208 | 0 | 0.2 |
| 1.902548 | 1.956661 | 1.673591 | 50 | 0.2 |
| 1.597808 | 1.663010 | 1.413974 | 100 | 0.2 |
| 2.759110 | 2.812890 | 2.416510 | 0 | 0.5 |
| 2.454370 | 2.519239 | 2.156893 | 50 | 0.5 |
| 2.149630 | 2.225588 | 1.897276 | 100 | 0.5 |